Transcript Missie en Visie TU Delft

Design and Vehicle Implementation
of an Adaptive ABS
J. Tigelaar
13 May 2011
the future
Design and Vehicle ImplementationChallenge
of an Adaptive
ABS 11
the future
Design and Vehicle ImplementationChallenge
of an Adaptive
ABS
2
Goal
…Taking the research one step further…
• Research on ABS is part of a larger research objective
• Load based vehicle dynamics control
• Decrease in system complexity
• Decrease in development cost
• Increase in performance
Novel ABS algorithm evaluated on testbench.
Limitations:
no changing load on tyre
no changing friction coefficient
Goal: Evaluate ABS robustness (Fz, µ) and implement in test vehicle
the future
Design and Vehicle ImplementationChallenge
of an Adaptive
ABS
3
Contents
The next ~25 min
• Why do we need ABS?
• How does it work?
• PART 1 -> Algorithm Design: increase its robustness
• Simulation evaluation
• PART 2 -> Vehicle Implementation: implement in test vehicle
• Track testing
• Does Load Based Sensing offer improvements in performance?
the future
Design and Vehicle ImplementationChallenge
of an Adaptive
ABS
4
Why does one need ABS?
ABS video
This video
contains
shocking
images and is
therefore not
suitable for
BMW fans.
Challenge
the future
Introduction
to ABS
5
Why does one need ABS?
ABS video
To prevent wheel-lock
What happens at wheel-lock?
This video
contains
shocking
images and is
therefore not
suitable for
BMW fans.
Challenge
the future
Introduction
to ABS
6
Braking
Forces are generated at tyre-road contact patch
• Many different models of tyre-road interaction
[Jazar, Reza N. (2008): Vehicle dynamics. Theory and application. New York, NY: Springer.]
Challenge
the future
Introduction
to ABS
7
Braking (Fx)
Longitudinal Tyre Forces
1.2
dry asphalt
wet asphalt
dry cobblestone
snow
Fx/Fz()
fric coeff
1
Fx  Fz   ( )
0.8
vx  r
r

 1
vx
vx
0.6
0.4
0.2
0
max Fx -> BD
0
0.2
0.4
0.6
0.8
1
slip  λ
Slip
Challenge
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Introduction
to ABS
8
Braking (Fy)
Lateral Tyre Forces
FY/FZ
F  F  ( ,  )
y
z
Steerability -> front
Stability -> rear
Slip λ
[Tanelli, M.; Corno, M.; Boniolo, I.; Savaresi, S.M. (2009): Active braking control of two-wheeled vehicles on curves.]
Challenge
the future
Introduction
to ABS
9
Re-cap
Trade-off between Fx and Fy
Prevent wheel-lock in
order to:
•Maintain steerability
Ideal operating range
•Maintain stability
Normalized force
•Decrease braking
distance
Many different
control strategies to
achieve this
Slip λ
Challenge
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Introduction
to ABS 10
How does ABS cycle?
Hold/Decrease/Increase pressure
How does ABS work?
Sensor
Wheel speed sensor
Control
Actuator
Valves
5 Phase : hybrid wheel deceleration based logic
Challenge
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Introduction
to ABS 11
A 2-Phase example
A hybrid wheel deceleration based logic
Normalized force
Continuous and discrete states
Slip λ
Challenge
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Algorithm
Design 12
ABS 5-Phase algorithm
(x1 and x2)
A hybrid wheel deceleration based logic
X1 : Wheel slip offset
X2 : Wheel acceleration offset
x1    
x2  r  ax*
*
1
x1  ( x2  ( *  x1 )ax* )
vx
b
x2    '( x1 )( x2  ( *  x1 )ax* )  u
vx
R2
b
Fz
J
R
u T
J
Set of dynamic equations
Challenge
the future
Algorithm
Design 13
5-Phase algorithm
A hybrid wheel deceleration based logic
Stating that
• Brake torque will either be kept constant or change rapidly
• Switching between torques is triggered by thresholds
• Thresholds are wheel deceleration based
Regulation logic chosen as such to keep unmeasured x1 small
close to 0
Challenge
the future
Algorithm
Design 14
5-Phase Automaton
The hybrid automaton
State transistions governed by guard conditions.
Evolution of continuous states is determined by dynamic system.
x1    *
x2  r  ax*
[Pasillas-Lépine, W. (2006): Hybrid modeling and limit cycle analysis for a class of five-phase anti-lock brake algorithms.]
Challenge
the future
Algorithm
Design 15
5-Phase 1st Integrals
Phase-plane (x1-x2) trajectories
For constant brake torque
R2
I1  x2  FZ  ( x1 )
J
For large torque variations,
Approximate first integral is
I 2  ln(1   *  x1 ) 
1
( x2  ( *  x1 )ax* )2
2u0
Approximation error
2
R
error   2 ( x2  ( *  x1 )ax* ) 2  (
Fz   ( x1 )  ax* )
J
with

1
u0
Challenge
the future
Algorithm
Design 16
5-Phase Criteria
The criteria
Criterion 1 :
Satisfying all 5
criteria
Criterion 2 :
Criterion 3 :
Stability
guaranteed
Criterion 4 :
Criterion 5 :
R2
b
Fz
J
Challenge
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Algorithm
Design 17
Load transfer
Static and Dynamic
static
FZ F ,R
hcog
l1
2 FZ2  mg  max
L
L
dynamic
hcog
l2,1
1
 (mg  max
)
2
L
L
hcog
l2
2 FZ1  mg  max
L
L
[Jazar, Reza N. (2008): Vehicle dynamics. Theory and application. New York, NY: Springer.]
Challenge
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Algorithm
Design 18
Fz and Stability
The fifth criteria
static
Load transfer [N]
FZ F ,R
dynamic
hcog
l2,1
1
 (mg  max
)
2
L
L
FZ 
FZ L
mghcog
Friction coefficient µ
• [B] Maintain stability at lower loads
• [C] Maintain stability at lower µ’s
-> retune (ε5) thresholds (limited)
-> T rate increase in phase 5
Challenge
the future
Algorithm
Design 19
5-Phase Simulation
Simulation results
• 4 wheel car model. Algorithm runs separately on each wheel.
• Straight line braking starting from 150 km/h
• Cost functions are defined as:
• Maximum slip
J ( )  max
Fy
• Braking distance
t1
J (s)   v(t ) dt
Fx
t0
Challenge
the Design
future 20
Algorithm
5-Phase Simulation at high µ
Simulation results (FL wheel)
• Observed that as µ increases, the algorithm
cycles within
Long tire force FL vs Slip
10000
stable zone.
Larger force drop
occurs during
cycling
Longitudinal
Tyre Force [N]
Long tyre force [N]
9000
8000
 = 0.4
 = 0.65
 = 0.9
7000
6000
5000
4000
3000
2000
1000
0
0
0.05
0.1
0.15
0.2
Slip 
0.25
0.3
0.35
Slip λ
Challenge
the Design
future 21
Algorithm
0.4
5-Phase Simulation Limit Cycle
Simulation results
ABS Phase-plane evolution (1st initial cycle 1:1900)
80
• 5th threshold
determines
the slip level
reached in the
unstable zone
2
[m/s
Wheel
acceleration
offset
Wheel
] 2]
acceleration offset
x2 [m/s
60
2
Phase 2
Phase 2
Phase 3
Phase 3
Phase 2
40
1
3
20
Phase 4
0
Phase 1
-20
Phase 5
4
-40
5
-60
-80
-100
-120
-40
ABS
Initialization
-35
-30
-25
-20
-10
-15
Slip offset x1 [%]
-5
0
5
10
Wheel slip offset x1 [%]
Challenge
the Design
future 22
Algorithm
15
5-Phase Simulation at low µ
Simulation results
• An increased ε5 would be beneficial forLongitudinal
higher
µ, but adverse
tire forces FL RR vs Slip
3500
for low µ
ε5 = 30
LF F
Longitudinal Tyre Force [N]
3000
A dynamic
threshold
LF Fx
x
RR Fx
ε5 = 50
RR Fx
2500
ε5 = 30
2000
1500
ε5 = 50
1000
500
0
0
0.05
0.1
0.15
0.2
slip 
0.25
0.3
0.35
Slip λ
Challenge
the Design
future 23
Algorithm
0.4
5-Phase Dynamic Threshold
Dynamic fifth threshold
• Fifth threshold can be freely defined
• Friction coefficient cannot be measured directly
ε5
FX
 ( ) 
FZ
 Fx 
  14.218
 Fz 
 5, Dynamic   5  28.527  max 
FX/FZ
Challenge
the Design
future 24
Algorithm
Adaptive 5-Phase Results
Results - improvements
• 20 % decrease in braking distance
• Maximum slip level is maintained
Braking distance for different threshold e5
Maximum slip for different threshold e5
0.2
e5=30
e5=50
e5=e5dyn
Theoretical
ε5=30
200
0.1
0.05
ε5=50
180
0.3
0.4
0.5
160
0.2
120
0.15
Theoretical
ε5=d ε5
80
60
0.3
0.9
1
0.4
0.5
0.6
0.7
0.8
Friction coefficient road 
Friction coefficient
e5=30
e5=50
e5=de5
0.1
rear
140
100
0.6
0.7
0.8
Friction coefficient road 
Friction coefficient
Slip 
Braking distance
Braking
distance[m]
[m]
220
0.15
Slip 
240
e5=30
e5=50
e5=de5
front
260
0.05
0.9
1
0.3
0.4
0.5
0.6
0.7
0.8
Friction coefficient road 
Friction coefficient
Challenge
the Design
future 25
Algorithm
0.9
1
Adaptive 5-Phase Video
Results - improvements
• VIDEOS – steering manoeuvre (2m to the left)
• White car
• Red car
• Blue car
:
:
:
No ABS
ABS with standard 5-Phase
ABS with adaptive 5-Phase
Lane change during braking from 150km/h
Challenge
the Design
future 26
Algorithm
Vehicle modifications (VM)
BMW 530 (E60)
Challenge
the future 27
Vehicle
Implementation
VM Principle
Basic principle
• Original ABS serves as a benchmark, driver can switch
ABS module
Individual wheel
control
Dummy
ABS module
• ECU (sensor input and required control decisions)
• Power stage (amplify the control signal)
• Hydraulic unit (actuate the solenoid valves)
How?
Challenge
the future 28
Vehicle
Implementation
VM dSpace
Basic principle
dSpace Autobox enables receiving and sending signals
IN:
wheel speed (tapped)
brake pressure (installed)
OUT:
control action valves
control action pump
Challenge
the future 29
Vehicle
Implementation
VM Brake circuit
Brake pressure sensors
2
3
1
6
5
7
1
Brake pedal
2
Master cylinder
3
Brake booster
4
Brake calliper
5
Return pump
6
Intake valve
7
Exhaust valve
8
Pressure sensor
8
4
Intake Valve (#6)
open
closed
closed
Exhaust Valve (#7)
closed
closed
open
Mode
Pressure build-up
Hold pressure
Pressure decrease
[Robert Bosch GmbH (2007): Automotive Electrics Automotive Electronics. 5th: Wiley.]
Challenge
the future 30
Vehicle
Implementation
VM T-split
Brake pressure sensors
Challenge
the future 31
Vehicle
Implementation
VM dSpace
Basic principle
dSpace Autobox enables receiving and sending signals
IN:
wheel speed (tapped)
brake pressure (installed)
OUT:
control action for valves (hold and decrease presssure)
control action for pump (increase pressure)
Challenge
the future 32
Vehicle
Implementation
VM ECU
The ABS Module
The ECU
Still being
reverse engineered.
Challenge
the future 33
Vehicle
Implementation
VM Conventional
System overview - conventional
Challenge
the future 34
Vehicle
Implementation
VM Novel
System overview - novel
Challenge
the future 35
Vehicle
Implementation
VM Track testing
Challenge
the future 36
Vehicle
Implementation
Conclusions
An overview
PART 1
• Through the use of load sensing:
Dynamic thresholds can significantly improve ABS performance
Decreased braking distance by 20%
Maintained lateral stability and steerability
PART 2
• Vehicle modification allow performance evaluation
Challenge Conclusion
the future 37
Challenge Conclusion
the future 38
Braking
The braking process
Challenge the future
39
Is ABS improvement worthwhile?
Challenge the future
40
Further research
TU Delft
• ABS Activation Logic
• Different than slip based (e.g. brake pedal)
• Coupling of all 4 wheels
• Synchronization on front
• A-Synchronization on rear
• Modeling of brake efficiency
• Thermal effects
ChallengeExtra
the future
Slides 41
Future of ABS
Possible outcomes
• From motion based to force based control systems
• Electro-Mechanical Brakes
• Force modulation is continous (not discrete)
• No vibrations to brake pedal (safety)
• No toxic brake fluid (leakage)
• From threshold based control rules (wheel deceleration)
move to slip control (multi-applications, ABS, ESP, TCS, …)
• ABS simplification
ChallengeExtra
the future
Slides 42
ABS Synchronization
Challenge the future
43
Controller Area Network (CAN)
Tests show a deviation in time interval reception, thus large jumps
in value
Average time interval over all 4 wheel signals = 60 msec
Time interval occurence
occurence
mean time interval
70
Wheel speed [km/h]
60
Occurrence
Occurance
50
40
30
20
10
0
Time [s]
0
0.05
0.1
0.15
0.2
Time steps
0.25
0.3
Time steps
Challenge the future
44
0.35
Wheel speed
Active Inductance Sensors
• Sense change in magnetic field due to incremental ring
• Contains 3 Hall sensors
• 1 and 3 serve for velocity estimation
• 2 serves for direction of rotation
• Derive wheel deceleration is challenging. Two main methods
• Lines-Per-Period
• Fixed-position
ChallengeExtra
the future
Slides 45
Bosch ABS
• Based on:
Heuristics
•Requires extensive tuning per vehicle model
Rule-of-Thumb
• System is unclear -> contains many control rules
System complexity ever increasing
• Also uses (amongst others) wheel deceleration values
Challenge the future
46
VM PWM
Basic principle
• Pulse-Width Modulated
signal for valve hold control
and pump increase control
50% duty cycle
• Additional I/O signal
for valve release control
Challenge the future
47